Prediction of surface location error in milling considering the effects of uncertain factors
Machining accuracy of a milled surface is influenced by process dynamics. Surface location error (SLE) in milling determines final dimensional accuracy of the finished surface. Therefore, it is critical to predict, control, and minimize SLE. In traditional methods, the effects of uncertain factor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-12-01
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| Series: | Mechanical Sciences |
| Online Access: | https://www.mech-sci.net/8/385/2017/ms-8-385-2017.pdf |
| Summary: | Machining accuracy of a milled surface is influenced by process dynamics.
Surface location error (SLE) in milling determines final dimensional accuracy
of the finished surface. Therefore, it is critical to predict, control, and
minimize SLE. In traditional methods, the effects of uncertain factors are
usually ignored during prediction of SLE, and this would tend to generate
estimation errors. In order to solve this problem, this paper presents
methods for probabilistic analysis of SLE in milling. A dynamic model for
milling process is built to determine relationship between SLE and cutting
parameters using full-discretization method (FDM). Monte-Carlo
simulation (MCS) method and artificial neural network (ANN) based MCS method
are proposed for predicting reliability of the milling process. Finally, a
numerical example is used to evaluate the accuracy and efficiency of the
proposed method. |
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| ISSN: | 2191-9151 2191-916X |